Introduction
maSigPro is a R package that initially was developed for the analysis of time series of multiple conditions. maSigPro follows a two steps regression strategy to find variable with significant temporal changes and significant differences between experimental conditions The method defines a general regression model for the data where the experimental conditions are identified by dummy variables.
The procedure first adjusts this global model by the least-squared technique to identify variables changing over time and selects significant ones applying false discovery rate control procedures.
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maSigPro identified 11 clusters of 283 variables in total.
Figure 1. Group averages of all genes in each cluster. Color corresponds to measurements (red = high).
Click to view full table of group means.
Figure 2. Inter-correlation between the means of all clusters. Similar clusters have higher correlation coefficient.
Figure 3. Cluster profiles.
Post-hoc analysis of the clusters by comparing conditions.
Table 1 The post-hoc analysis of clusters shown in Figure 3. The between statistics represent the maximal deviation (when and how much) between a time point and the beginning time point, and the within statistics represent the maximal deviation (when and how much) of the conditions from each other within the same time point.
| Cluster | Size | Between_When | Between_Dev | Between_P | Within_When | Within_Dev | Within_P |
|---|---|---|---|---|---|---|---|
| Cluster_1 | 15 | T120 | 1.972 | 2.40e-03 | T120 | 0.5566 | 1.02e-02 |
| Cluster_2 | 70 | T120 | 2.700 | 1.59e-02 | T150 | 0.8227 | 1.24e-02 |
| Cluster_3 | 19 | T30 | 2.452 | 2.96e-04 | T60 | 0.7509 | 9.23e-05 |
| Cluster_4 | 20 | T60 | 4.324 | 1.24e-10 | T30 | 0.3483 | 5.33e-02 |
| Cluster_5 | 9 | T120 | 5.128 | 1.16e-10 | T30 | 0.3888 | 7.58e-02 |
| Cluster_6 | 7 | T90 | 2.826 | 7.34e-06 | T15 | 0.7261 | 7.06e-04 |
| Cluster_7 | 68 | T120 | 2.289 | 3.56e-02 | T150 | 0.6380 | 4.43e-02 |
| Cluster_8 | 19 | T90 | 2.341 | 8.22e-03 | T150 | 0.6411 | 1.34e-02 |
| Cluster_9 | 21 | T120 | 2.644 | 1.09e-02 | T150 | 0.8561 | 9.10e-03 |
| Cluster_10 | 23 | T120 | 2.563 | 5.96e-03 | T60 | 0.7415 | 8.62e-03 |
| Cluster_11 | 12 | T120 | 1.840 | 2.86e-02 | T60 | 0.6601 | 2.04e-02 |
Figure 4. The left panel shows when and how much the maximal deviation happens for each cluster between the beginning time point and all the other time points. The right panel shows when and how much the maximal deviation between conditions happens within each time point.
If the variables can be classified into pre-defined sets, such as genes into Gene Ontology categories, over-representation analysis can be run to test the enrichment of sets in each cluster.
Table 2 Numbers of pre-defined variable sets significantly enriched in each cluster. Sets were split based on their sources. Click on each number to see list of the variable sets.
| BioSystems | KEGG | MSigDb | PubTator | |
|---|---|---|---|---|
| Cluster_1 | 76 | 10 | 59 | 27 |
| Cluster_2 | 118 | 15 | 102 | 5 |
| Cluster_3 | 93 | 7 | 96 | 34 |
| Cluster_4 | 145 | 7 | 98 | 92 |
| Cluster_5 | 66 | 1 | 122 | 39 |
| Cluster_6 | 70 | 0 | 44 | 12 |
| Cluster_7 | 153 | 6 | 193 | 17 |
| Cluster_8 | 34 | 6 | 80 | 69 |
| Cluster_9 | 88 | 3 | 172 | 28 |
| Cluster_10 | 65 | 11 | 123 | 72 |
| Cluster_11 | 40 | 1 | 45 | 26 |
Check out the RoCA home page for more information.
To reproduce this report:
Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory
To generate a new report using your own input data and parameter, edit the following items in the YAML file:
Run the code below within R Console or RStudio, preferablly with a new R session:
if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }
CreateReport(filename.yaml); # filename.yaml is the YAML file you just downloaded and edited
If there is no complaint, go to the output folder and open the index.html file to view report.
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] gplots_3.0.1 maSigPro_1.42.0 DynDoc_1.48.0
## [4] widgetTools_1.48.0 MASS_7.3-45 Biobase_2.30.0
## [7] GenomicRanges_1.22.4 GenomeInfoDb_1.6.3 IRanges_2.4.8
## [10] S4Vectors_0.8.11 BiocGenerics_0.16.1 htmlwidgets_0.9
## [13] DT_0.2 yaml_2.1.13 rmarkdown_1.3
## [16] knitr_1.17 RoCA_0.0.0.9000 awsomics_0.0.0.9000
## [19] RCurl_1.95-4.8 bitops_1.0-6 devtools_1.13.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.13 highr_0.6 XVector_0.10.0
## [4] tkWidgets_1.48.0 tools_3.2.2 zlibbioc_1.16.0
## [7] digest_0.6.12 jsonlite_1.0 evaluate_0.10.1
## [10] memoise_1.1.0 withr_2.1.0 stringr_1.2.0
## [13] gtools_3.5.0 caTools_1.17.1 rprojroot_1.2
## [16] limma_3.26.9 gdata_2.17.0 magrittr_1.5
## [19] backports_1.1.1 htmltools_0.3.6 KernSmooth_2.23-15
## [22] stringi_1.1.1 Mfuzz_2.30.0
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